论文标题
文本分类的语义互动学习：一种用于上下文互动的建设性方法
Semantic Interactive Learning for Text Classification: A Constructive Approach for Contextual Interactions
论文作者
论文摘要
交互式机器学习（IML）应使智能系统能够从其最终用户进行交互学习，并迅速变得越来越重要。尽管它将人类置于循环中，但相互作用主要是通过错过上下文信息的相互解释进行的。此外，Caipi等当前的模型IML策略仅限于“破坏性”反馈，这意味着它们仅允许专家阻止学习者使用无关的功能。在这项工作中，我们提出了一个新颖的互动框架，称为文本域的语义互动学习。我们将构建性和上下文反馈纳入学习者的问题将（a）构建到学习者中，该架构（a）可以在人与机器之间进行更多的语义对齐，以及（b）同时有助于维持输入域的统计特征，以生成基于有意义的纠正的用户定义的conlectamples时生成用户定义的柜台。因此，我们介绍了一种称为Semanticpush的技术，该技术可有效地将人类对人类的概念校正转换为非排除培训示例，以便将学习者的推理推向所需的行为。在几个实验中，我们表明我们的方法在预测性能以及下游多级分类任务中的局部解释质量方面显然优于Caipi（一种先进的IML策略）。
Interactive Machine Learning (IML) shall enable intelligent systems to interactively learn from their end-users, and is quickly becoming more and more important. Although it puts the human in the loop, interactions are mostly performed via mutual explanations that miss contextual information. Furthermore, current model-agnostic IML strategies like CAIPI are limited to 'destructive' feedback, meaning they solely allow an expert to prevent a learner from using irrelevant features. In this work, we propose a novel interaction framework called Semantic Interactive Learning for the text domain. We frame the problem of incorporating constructive and contextual feedback into the learner as a task to find an architecture that (a) enables more semantic alignment between humans and machines and (b) at the same time helps to maintain statistical characteristics of the input domain when generating user-defined counterexamples based on meaningful corrections. Therefore, we introduce a technique called SemanticPush that is effective for translating conceptual corrections of humans to non-extrapolating training examples such that the learner's reasoning is pushed towards the desired behavior. In several experiments, we show that our method clearly outperforms CAIPI, a state of the art IML strategy, in terms of Predictive Performance as well as Local Explanation Quality in downstream multi-class classification tasks.
